Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations21704
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory176.0 B

Variable types

Categorical3
DateTime1
Numeric19

Alerts

AQI is highly overall correlated with AQI_Bucket and 6 other fieldsHigh correlation
AQI_Bucket is highly overall correlated with AQIHigh correlation
Benzene is highly overall correlated with TolueneHigh correlation
CO is highly overall correlated with AQIHigh correlation
NO is highly overall correlated with AQI and 2 other fieldsHigh correlation
NO2 is highly overall correlated with AQI and 3 other fieldsHigh correlation
NOx is highly overall correlated with NO and 1 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 4 other fieldsHigh correlation
PM2.5_rolling is highly overall correlated with AQI and 3 other fieldsHigh correlation
PM_ratio is highly overall correlated with PM2.5 and 1 other fieldsHigh correlation
Season is highly overall correlated with monthHigh correlation
Toluene is highly overall correlated with BenzeneHigh correlation
month is highly overall correlated with SeasonHigh correlation
Benzene is highly skewed (γ1 = 22.06727317)Skewed
PM_ratio is highly skewed (γ1 = 34.54368464)Skewed
NOx has 336 (1.5%) zerosZeros
CO has 332 (1.5%) zerosZeros
Benzene has 1761 (8.1%) zerosZeros
Toluene has 1057 (4.9%) zerosZeros
Xylene has 223 (1.0%) zerosZeros
day_of_week has 3045 (14.0%) zerosZeros

Reproduction

Analysis started2026-02-15 14:29:12.998314
Analysis finished2026-02-15 14:31:36.210450
Duration2 minutes and 23.21 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

City
Categorical

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size339.1 KiB
Delhi
1897 
Hyderabad
1875 
Chennai
1865 
Bengaluru
1774 
Lucknow
1580 
Other values (19)
12713 

Length

Max length18
Median length12
Mean length8.2931257
Min length5

Characters and Unicode

Total characters179994
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAhmedabad
2nd rowAhmedabad
3rd rowAhmedabad
4th rowAhmedabad
5th rowAhmedabad

Common Values

ValueCountFrequency (%)
Delhi1897
 
8.7%
Hyderabad1875
 
8.6%
Chennai1865
 
8.6%
Bengaluru1774
 
8.2%
Lucknow1580
 
7.3%
Patna1368
 
6.3%
Ahmedabad1211
 
5.6%
Visakhapatnam1152
 
5.3%
Thiruvananthapuram1049
 
4.8%
Jaipur1001
 
4.6%
Other values (14)6932
31.9%

Length

2026-02-15T20:01:36.482887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
delhi1897
 
8.7%
hyderabad1875
 
8.6%
chennai1865
 
8.6%
bengaluru1774
 
8.2%
lucknow1580
 
7.3%
patna1368
 
6.3%
ahmedabad1211
 
5.6%
visakhapatnam1152
 
5.3%
thiruvananthapuram1049
 
4.8%
jaipur1001
 
4.6%
Other values (14)6932
31.9%

Most occurring characters

ValueCountFrequency (%)
a33946
18.9%
r13904
 
7.7%
n12822
 
7.1%
u11416
 
6.3%
i10735
 
6.0%
h10467
 
5.8%
e9442
 
5.2%
m6919
 
3.8%
t6600
 
3.7%
d6467
 
3.6%
Other values (27)57276
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)179994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a33946
18.9%
r13904
 
7.7%
n12822
 
7.1%
u11416
 
6.3%
i10735
 
6.0%
h10467
 
5.8%
e9442
 
5.2%
m6919
 
3.8%
t6600
 
3.7%
d6467
 
3.6%
Other values (27)57276
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)179994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a33946
18.9%
r13904
 
7.7%
n12822
 
7.1%
u11416
 
6.3%
i10735
 
6.0%
h10467
 
5.8%
e9442
 
5.2%
m6919
 
3.8%
t6600
 
3.7%
d6467
 
3.6%
Other values (27)57276
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)179994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a33946
18.9%
r13904
 
7.7%
n12822
 
7.1%
u11416
 
6.3%
i10735
 
6.0%
h10467
 
5.8%
e9442
 
5.2%
m6919
 
3.8%
t6600
 
3.7%
d6467
 
3.6%
Other values (27)57276
31.8%

Date
Date

Distinct2009
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size339.1 KiB
Minimum2015-01-01 00:00:00
Maximum2020-07-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-15T20:01:36.912599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:37.467227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation 

Distinct10851
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.490206
Minimum0.16
Maximum914.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:38.133080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile13.4215
Q128.82
median48.705
Q379.7725
95-th percentile189.4885
Maximum914.94
Range914.78
Interquartile range (IQR)50.9525

Descriptive statistics

Standard deviation60.865597
Coefficient of variation (CV)0.91540696
Kurtosis12.572468
Mean66.490206
Median Absolute Deviation (MAD)23.185
Skewness2.7919349
Sum1443103.4
Variance3704.621
MonotonicityNot monotonic
2026-02-15T20:01:38.371378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.45057795112
 
0.5%
20.7512
 
0.1%
27.8211
 
0.1%
29.7510
 
< 0.1%
47.4310
 
< 0.1%
33.99
 
< 0.1%
22.939
 
< 0.1%
31.089
 
< 0.1%
21.059
 
< 0.1%
28.459
 
< 0.1%
Other values (10841)21504
99.1%
ValueCountFrequency (%)
0.161
< 0.1%
0.241
< 0.1%
0.281
< 0.1%
0.981
< 0.1%
0.991
< 0.1%
1.141
< 0.1%
1.191
< 0.1%
1.251
< 0.1%
1.391
< 0.1%
1.41
< 0.1%
ValueCountFrequency (%)
914.941
< 0.1%
685.361
< 0.1%
674.431
< 0.1%
645.51
< 0.1%
639.191
< 0.1%
588.391
< 0.1%
582.281
< 0.1%
571.021
< 0.1%
561.421
< 0.1%
555.331
< 0.1%

PM10
Real number (ℝ)

High correlation 

Distinct11153
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.00193
Minimum0.18
Maximum847.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:38.610474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile31
Q170.3875
median118.1271
Q3122.965
95-th percentile270.018
Maximum847.41
Range847.23
Interquartile range (IQR)52.5775

Descriptive statistics

Standard deviation75.554189
Coefficient of variation (CV)0.64027929
Kurtosis8.2512745
Mean118.00193
Median Absolute Deviation (MAD)31.805
Skewness2.248427
Sum2561113.8
Variance5708.4354
MonotonicityNot monotonic
2026-02-15T20:01:39.013624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118.12710296035
 
27.8%
949
 
< 0.1%
33.817
 
< 0.1%
39.466
 
< 0.1%
20.536
 
< 0.1%
102.176
 
< 0.1%
87.026
 
< 0.1%
92.45
 
< 0.1%
66.955
 
< 0.1%
46.545
 
< 0.1%
Other values (11143)15614
71.9%
ValueCountFrequency (%)
0.181
< 0.1%
0.192
< 0.1%
0.21
< 0.1%
0.212
< 0.1%
0.221
< 0.1%
0.231
< 0.1%
0.241
< 0.1%
0.262
< 0.1%
0.271
< 0.1%
0.281
< 0.1%
ValueCountFrequency (%)
847.411
< 0.1%
796.881
< 0.1%
763.581
< 0.1%
761.911
< 0.1%
743.981
< 0.1%
708.81
< 0.1%
706.581
< 0.1%
693.211
< 0.1%
666.721
< 0.1%
654.681
< 0.1%

NO
Real number (ℝ)

High correlation 

Distinct5475
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.920361
Minimum0.03
Maximum287.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:39.265123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1.74
Q15.51
median10.06
Q320.18
95-th percentile64.0695
Maximum287.14
Range287.11
Interquartile range (IQR)14.67

Descriptive statistics

Standard deviation22.819578
Coefficient of variation (CV)1.2733883
Kurtosis15.207235
Mean17.920361
Median Absolute Deviation (MAD)5.83
Skewness3.2890283
Sum388943.51
Variance520.73313
MonotonicityNot monotonic
2026-02-15T20:01:39.667753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9426
 
0.1%
0.9226
 
0.1%
5.2325
 
0.1%
0.924
 
0.1%
5.2824
 
0.1%
0.9724
 
0.1%
2.8123
 
0.1%
6.2922
 
0.1%
5.722
 
0.1%
7.7822
 
0.1%
Other values (5465)21466
98.9%
ValueCountFrequency (%)
0.031
< 0.1%
0.061
< 0.1%
0.11
< 0.1%
0.111
< 0.1%
0.141
< 0.1%
0.181
< 0.1%
0.191
< 0.1%
0.21
< 0.1%
0.211
< 0.1%
0.231
< 0.1%
ValueCountFrequency (%)
287.141
< 0.1%
270.091
< 0.1%
268.031
< 0.1%
246.661
< 0.1%
221.741
< 0.1%
221.411
< 0.1%
221.031
< 0.1%
220.861
< 0.1%
217.851
< 0.1%
215.421
< 0.1%

NO2
Real number (ℝ)

High correlation 

Distinct7137
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.934599
Minimum0.01
Maximum277.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:39.984620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile5.27
Q112.95
median23.35
Q339.3825
95-th percentile76.3285
Maximum277.31
Range277.3
Interquartile range (IQR)26.4325

Descriptive statistics

Standard deviation24.691333
Coefficient of variation (CV)0.82484263
Kurtosis8.6548111
Mean29.934599
Median Absolute Deviation (MAD)12.03
Skewness2.2427299
Sum649700.53
Variance609.66193
MonotonicityNot monotonic
2026-02-15T20:01:40.340310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5817
 
0.1%
13.915
 
0.1%
13.2714
 
0.1%
10.9714
 
0.1%
14.1914
 
0.1%
11.6813
 
0.1%
11.2213
 
0.1%
10.0113
 
0.1%
10.0913
 
0.1%
22.0913
 
0.1%
Other values (7127)21565
99.4%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.052
 
< 0.1%
0.061
 
< 0.1%
0.076
< 0.1%
0.084
< 0.1%
0.095
< 0.1%
0.12
 
< 0.1%
0.112
 
< 0.1%
ValueCountFrequency (%)
277.311
< 0.1%
273.391
< 0.1%
266.461
< 0.1%
245.621
< 0.1%
239.181
< 0.1%
239.11
< 0.1%
236.791
< 0.1%
229.451
< 0.1%
220.261
< 0.1%
219.011
< 0.1%

NOx
Real number (ℝ)

High correlation  Zeros 

Distinct7668
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.788049
Minimum0
Maximum293.1
Zeros336
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:40.724664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.0615
Q113.65
median23.92
Q340.75
95-th percentile96.184
Maximum293.1
Range293.1
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation30.873716
Coefficient of variation (CV)0.94161492
Kurtosis7.6745894
Mean32.788049
Median Absolute Deviation (MAD)12.37
Skewness2.3456565
Sum711631.82
Variance953.18637
MonotonicityNot monotonic
2026-02-15T20:01:41.043069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0336
 
1.5%
4.22181
 
0.8%
6.24110
 
0.5%
4.329
 
0.1%
2.2127
 
0.1%
4.1415
 
0.1%
4.4715
 
0.1%
16.7814
 
0.1%
4.9713
 
0.1%
15.1312
 
0.1%
Other values (7658)20952
96.5%
ValueCountFrequency (%)
0336
1.5%
0.051
 
< 0.1%
0.061
 
< 0.1%
0.171
 
< 0.1%
0.211
 
< 0.1%
0.221
 
< 0.1%
0.281
 
< 0.1%
0.311
 
< 0.1%
0.361
 
< 0.1%
0.382
 
< 0.1%
ValueCountFrequency (%)
293.11
< 0.1%
287.891
< 0.1%
262.321
< 0.1%
259.541
< 0.1%
257.821
< 0.1%
254.81
< 0.1%
252.141
< 0.1%
251.961
< 0.1%
244.341
< 0.1%
240.121
< 0.1%

NH3
Real number (ℝ)

Distinct5724
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.559453
Minimum0.02
Maximum352.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:41.280395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile3.49
Q111.8975
median23.483476
Q326.51
95-th percentile58.067
Maximum352.89
Range352.87
Interquartile range (IQR)14.6125

Descriptive statistics

Standard deviation23.107064
Coefficient of variation (CV)0.94086232
Kurtosis36.838875
Mean24.559453
Median Absolute Deviation (MAD)9.19
Skewness4.6912817
Sum533038.38
Variance533.93642
MonotonicityNot monotonic
2026-02-15T20:01:41.531360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.483476025212
 
24.0%
10.4622
 
0.1%
10.4220
 
0.1%
3.6619
 
0.1%
3.6517
 
0.1%
3.6415
 
0.1%
11.9915
 
0.1%
11.715
 
0.1%
514
 
0.1%
12.0614
 
0.1%
Other values (5714)16341
75.3%
ValueCountFrequency (%)
0.021
 
< 0.1%
0.041
 
< 0.1%
0.061
 
< 0.1%
0.081
 
< 0.1%
0.11
 
< 0.1%
0.121
 
< 0.1%
0.141
 
< 0.1%
0.153
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
ValueCountFrequency (%)
352.891
< 0.1%
328.891
< 0.1%
323.481
< 0.1%
309.041
< 0.1%
303.531
< 0.1%
302.081
< 0.1%
301.281
< 0.1%
301.181
< 0.1%
297.641
< 0.1%
296.431
< 0.1%

CO
Real number (ℝ)

High correlation  Zeros 

Distinct1582
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3689219
Minimum0
Maximum175.81
Zeros332
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:41.767890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18
Q10.62
median0.94
Q31.47
95-th percentile8.43
Maximum175.81
Range175.81
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation7.0701301
Coefficient of variation (CV)2.984535
Kurtosis102.70975
Mean2.3689219
Median Absolute Deviation (MAD)0.39
Skewness8.6097132
Sum51415.08
Variance49.98674
MonotonicityNot monotonic
2026-02-15T20:01:42.158557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0332
 
1.5%
0.85200
 
0.9%
0.8191
 
0.9%
0.68190
 
0.9%
0.84187
 
0.9%
0.89184
 
0.8%
0.64182
 
0.8%
0.82181
 
0.8%
0.79181
 
0.8%
0.81180
 
0.8%
Other values (1572)19696
90.7%
ValueCountFrequency (%)
0332
1.5%
0.0157
 
0.3%
0.0250
 
0.2%
0.0347
 
0.2%
0.0425
 
0.1%
0.0543
 
0.2%
0.0639
 
0.2%
0.0733
 
0.2%
0.0828
 
0.1%
0.0934
 
0.2%
ValueCountFrequency (%)
175.811
< 0.1%
145.321
< 0.1%
134.851
< 0.1%
124.011
< 0.1%
119.681
< 0.1%
118.021
< 0.1%
115.871
< 0.1%
112.711
< 0.1%
108.991
< 0.1%
108.811
< 0.1%

SO2
Real number (ℝ)

Distinct4254
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.060786
Minimum0.01
Maximum186.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:42.436621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.8
Q15.89
median9.36
Q314.83
95-th percentile43.42
Maximum186.08
Range186.07
Interquartile range (IQR)8.94

Descriptive statistics

Standard deviation16.838655
Coefficient of variation (CV)1.1975614
Kurtosis23.606384
Mean14.060786
Median Absolute Deviation (MAD)4
Skewness4.219347
Sum305175.29
Variance283.54029
MonotonicityNot monotonic
2026-02-15T20:01:42.888301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6130
 
0.1%
5.9529
 
0.1%
5.8729
 
0.1%
5.7429
 
0.1%
5.5728
 
0.1%
5.8127
 
0.1%
4.6527
 
0.1%
6.1227
 
0.1%
5.1326
 
0.1%
8.7126
 
0.1%
Other values (4244)21426
98.7%
ValueCountFrequency (%)
0.011
< 0.1%
0.211
< 0.1%
0.361
< 0.1%
0.412
< 0.1%
0.421
< 0.1%
0.441
< 0.1%
0.511
< 0.1%
0.521
< 0.1%
0.551
< 0.1%
0.561
< 0.1%
ValueCountFrequency (%)
186.081
< 0.1%
178.581
< 0.1%
176.881
< 0.1%
174.831
< 0.1%
172.681
< 0.1%
168.451
< 0.1%
168.441
< 0.1%
167.132
< 0.1%
166.391
< 0.1%
163.21
< 0.1%

O3
Real number (ℝ)

Distinct7328
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.261689
Minimum0.01
Maximum257.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:43.154498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile7.68
Q119.88
median31.885
Q346.3825
95-th percentile74.05
Maximum257.73
Range257.72
Interquartile range (IQR)26.5025

Descriptive statistics

Standard deviation21.362354
Coefficient of variation (CV)0.60582333
Kurtosis3.4936049
Mean35.261689
Median Absolute Deviation (MAD)12.915
Skewness1.2981613
Sum765319.69
Variance456.35015
MonotonicityNot monotonic
2026-02-15T20:01:43.403685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.4817
 
0.1%
19.6414
 
0.1%
23.614
 
0.1%
22.1414
 
0.1%
18.3313
 
0.1%
32.0613
 
0.1%
43.7712
 
0.1%
22.9412
 
0.1%
13.1412
 
0.1%
28.2411
 
0.1%
Other values (7318)21572
99.4%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.041
 
< 0.1%
0.051
 
< 0.1%
0.061
 
< 0.1%
0.071
 
< 0.1%
0.18
< 0.1%
0.111
 
< 0.1%
0.121
 
< 0.1%
0.182
 
< 0.1%
0.191
 
< 0.1%
ValueCountFrequency (%)
257.731
< 0.1%
200.411
< 0.1%
193.311
< 0.1%
186.071
< 0.1%
177.071
< 0.1%
175.041
< 0.1%
169.361
< 0.1%
169.351
< 0.1%
162.431
< 0.1%
162.331
< 0.1%

Benzene
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1797
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5662257
Minimum0
Maximum455.03
Zeros1761
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:43.892635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.35
median1.7
Q33.2808403
95-th percentile9.76
Maximum455.03
Range455.03
Interquartile range (IQR)2.9308403

Descriptive statistics

Standard deviation15.804608
Coefficient of variation (CV)4.4317464
Kurtosis560.29202
Mean3.5662257
Median Absolute Deviation (MAD)1.57
Skewness22.067273
Sum77401.363
Variance249.78563
MonotonicityNot monotonic
2026-02-15T20:01:44.144597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2808403052288
 
10.5%
01761
 
8.1%
0.02209
 
1.0%
0.01179
 
0.8%
0.03170
 
0.8%
0.09157
 
0.7%
2157
 
0.7%
0.05152
 
0.7%
0.1147
 
0.7%
0.08147
 
0.7%
Other values (1787)16337
75.3%
ValueCountFrequency (%)
01761
8.1%
0.01179
 
0.8%
0.02209
 
1.0%
0.03170
 
0.8%
0.04122
 
0.6%
0.05152
 
0.7%
0.06123
 
0.6%
0.07105
 
0.5%
0.08147
 
0.7%
0.09157
 
0.7%
ValueCountFrequency (%)
455.031
< 0.1%
454.851
< 0.1%
449.381
< 0.1%
448.591
< 0.1%
445.831
< 0.1%
443.631
< 0.1%
438.011
< 0.1%
435.91
< 0.1%
435.091
< 0.1%
432.941
< 0.1%

Toluene
Real number (ℝ)

High correlation  Zeros 

Distinct3445
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.570925
Minimum0
Maximum454.85
Zeros1057
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:44.387086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q11.53
median6.36
Q38.7009721
95-th percentile32.49
Maximum454.85
Range454.85
Interquartile range (IQR)7.1709721

Descriptive statistics

Standard deviation19.267902
Coefficient of variation (CV)2.0131704
Kurtosis245.2406
Mean9.570925
Median Absolute Deviation (MAD)4.2
Skewness12.630577
Sum207727.36
Variance371.25207
MonotonicityNot monotonic
2026-02-15T20:01:44.651543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.700972084347
 
20.0%
01057
 
4.9%
0.02100
 
0.5%
0.0590
 
0.4%
0.0385
 
0.4%
679
 
0.4%
1.178
 
0.4%
0.0468
 
0.3%
0.0864
 
0.3%
0.0661
 
0.3%
Other values (3435)15675
72.2%
ValueCountFrequency (%)
01057
4.9%
0.0153
 
0.2%
0.02100
 
0.5%
0.0385
 
0.4%
0.0468
 
0.3%
0.0590
 
0.4%
0.0661
 
0.3%
0.0747
 
0.2%
0.0864
 
0.3%
0.0943
 
0.2%
ValueCountFrequency (%)
454.851
< 0.1%
454.121
< 0.1%
449.141
< 0.1%
448.871
< 0.1%
445.841
< 0.1%
443.631
< 0.1%
437.771
< 0.1%
435.941
< 0.1%
434.921
< 0.1%
433.021
< 0.1%

Xylene
Real number (ℝ)

Zeros 

Distinct1430
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2042843
Minimum0
Maximum170.37
Zeros223
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:45.043988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q12.16
median3.0701278
Q33.0701278
95-th percentile7.16
Maximum170.37
Range170.37
Interquartile range (IQR)0.91012782

Descriptive statistics

Standard deviation4.0680514
Coefficient of variation (CV)1.2695663
Kurtosis305.07613
Mean3.2042843
Median Absolute Deviation (MAD)0
Skewness12.586452
Sum69545.786
Variance16.549042
MonotonicityNot monotonic
2026-02-15T20:01:45.299311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.07012782312879
59.3%
0.1230
 
1.1%
0223
 
1.0%
2132
 
0.6%
0.12100
 
0.5%
0.1187
 
0.4%
0.6577
 
0.4%
0.1373
 
0.3%
0.1572
 
0.3%
0.1671
 
0.3%
Other values (1420)7760
35.8%
ValueCountFrequency (%)
0223
1.0%
0.0152
 
0.2%
0.0237
 
0.2%
0.0342
 
0.2%
0.0440
 
0.2%
0.0547
 
0.2%
0.0652
 
0.2%
0.0767
 
0.3%
0.0856
 
0.3%
0.0957
 
0.3%
ValueCountFrequency (%)
170.371
< 0.1%
125.181
< 0.1%
116.621
< 0.1%
109.231
< 0.1%
105.761
< 0.1%
94.481
< 0.1%
89.71
< 0.1%
84.721
< 0.1%
81.261
< 0.1%
73.391
< 0.1%

AQI
Real number (ℝ)

High correlation 

Distinct801
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.08796
Minimum14
Maximum1917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:45.670152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q180
median117
Q3207
95-th percentile406
Maximum1917
Range1903
Interquartile range (IQR)127

Descriptive statistics

Standard deviation140.76448
Coefficient of variation (CV)0.84752974
Kurtosis20.247657
Mean166.08796
Median Absolute Deviation (MAD)47
Skewness3.3546411
Sum3604773
Variance19814.64
MonotonicityNot monotonic
2026-02-15T20:01:45.996688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102193
 
0.9%
100191
 
0.9%
70189
 
0.9%
106181
 
0.8%
66176
 
0.8%
78175
 
0.8%
74175
 
0.8%
68167
 
0.8%
90167
 
0.8%
82166
 
0.8%
Other values (791)19924
91.8%
ValueCountFrequency (%)
142
 
< 0.1%
161
 
< 0.1%
172
 
< 0.1%
182
 
< 0.1%
1922
0.1%
2023
0.1%
216
 
< 0.1%
228
 
< 0.1%
233
 
< 0.1%
249
 
< 0.1%
ValueCountFrequency (%)
19171
< 0.1%
17471
< 0.1%
17191
< 0.1%
16721
< 0.1%
16461
< 0.1%
16301
< 0.1%
16131
< 0.1%
15771
< 0.1%
15581
< 0.1%
15371
< 0.1%

AQI_Bucket
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.1 KiB
Moderate
7650 
Satisfactory
7318 
Poor
2436 
Very Poor
2025 
Severe
1147 

Length

Max length12
Median length9
Mean length8.67946
Min length4

Characters and Unicode

Total characters188379
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowVery Poor
3rd rowSevere
4th rowSevere
5th rowSevere

Common Values

ValueCountFrequency (%)
Moderate7650
35.2%
Satisfactory7318
33.7%
Poor2436
 
11.2%
Very Poor2025
 
9.3%
Severe1147
 
5.3%
Good1128
 
5.2%

Length

2026-02-15T20:01:46.271738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T20:01:46.437261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate7650
32.2%
satisfactory7318
30.8%
poor4461
18.8%
very2025
 
8.5%
severe1147
 
4.8%
good1128
 
4.8%

Most occurring characters

ValueCountFrequency (%)
o26146
13.9%
r22601
12.0%
t22286
11.8%
a22286
11.8%
e20766
11.0%
y9343
 
5.0%
d8778
 
4.7%
S8465
 
4.5%
M7650
 
4.1%
i7318
 
3.9%
Other values (8)32740
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)188379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o26146
13.9%
r22601
12.0%
t22286
11.8%
a22286
11.8%
e20766
11.0%
y9343
 
5.0%
d8778
 
4.7%
S8465
 
4.5%
M7650
 
4.1%
i7318
 
3.9%
Other values (8)32740
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)188379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o26146
13.9%
r22601
12.0%
t22286
11.8%
a22286
11.8%
e20766
11.0%
y9343
 
5.0%
d8778
 
4.7%
S8465
 
4.5%
M7650
 
4.1%
i7318
 
3.9%
Other values (8)32740
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)188379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o26146
13.9%
r22601
12.0%
t22286
11.8%
a22286
11.8%
e20766
11.0%
y9343
 
5.0%
d8778
 
4.7%
S8465
 
4.5%
M7650
 
4.1%
i7318
 
3.9%
Other values (8)32740
17.4%

year
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.093
Minimum2015
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.3 KiB
2026-02-15T20:01:46.650145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2018
Q32019
95-th percentile2020
Maximum2020
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5056328
Coefficient of variation (CV)0.00074606709
Kurtosis-0.60669147
Mean2018.093
Median Absolute Deviation (MAD)1
Skewness-0.61659515
Sum43800691
Variance2.2669301
MonotonicityNot monotonic
2026-02-15T20:01:46.974168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20196524
30.1%
20185066
23.3%
20203831
17.7%
20162280
 
10.5%
20172201
 
10.1%
20151802
 
8.3%
ValueCountFrequency (%)
20151802
 
8.3%
20162280
 
10.5%
20172201
 
10.1%
20185066
23.3%
20196524
30.1%
20203831
17.7%
ValueCountFrequency (%)
20203831
17.7%
20196524
30.1%
20185066
23.3%
20172201
 
10.1%
20162280
 
10.5%
20151802
 
8.3%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3114173
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.3 KiB
2026-02-15T20:01:47.169108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4747404
Coefficient of variation (CV)0.55054835
Kurtosis-1.2163138
Mean6.3114173
Median Absolute Deviation (MAD)3
Skewness0.11174924
Sum136983
Variance12.073821
MonotonicityNot monotonic
2026-02-15T20:01:47.333594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
52085
9.6%
32036
9.4%
62008
9.3%
11934
8.9%
41914
8.8%
21829
8.4%
121805
8.3%
111787
8.2%
101741
8.0%
71579
7.3%
Other values (2)2986
13.8%
ValueCountFrequency (%)
11934
8.9%
21829
8.4%
32036
9.4%
41914
8.8%
52085
9.6%
62008
9.3%
71579
7.3%
81500
6.9%
91486
6.8%
101741
8.0%
ValueCountFrequency (%)
121805
8.3%
111787
8.2%
101741
8.0%
91486
6.8%
81500
6.9%
71579
7.3%
62008
9.3%
52085
9.6%
41914
8.8%
32036
9.4%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.766449
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.3 KiB
2026-02-15T20:01:47.514410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8386165
Coefficient of variation (CV)0.56059654
Kurtosis-1.2025176
Mean15.766449
Median Absolute Deviation (MAD)8
Skewness0.00049939068
Sum342195
Variance78.121141
MonotonicityNot monotonic
2026-02-15T20:01:47.756833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1738
 
3.4%
28727
 
3.3%
27723
 
3.3%
23720
 
3.3%
4718
 
3.3%
24717
 
3.3%
26716
 
3.3%
19715
 
3.3%
22713
 
3.3%
5713
 
3.3%
Other values (21)14504
66.8%
ValueCountFrequency (%)
1738
3.4%
2711
3.3%
3711
3.3%
4718
3.3%
5713
3.3%
6699
3.2%
7711
3.3%
8712
3.3%
9708
3.3%
10705
3.2%
ValueCountFrequency (%)
31420
1.9%
30662
3.1%
29700
3.2%
28727
3.3%
27723
3.3%
26716
3.3%
25707
3.3%
24717
3.3%
23720
3.3%
22713
3.3%

day_of_week
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9998618
Minimum0
Maximum6
Zeros3045
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size254.3 KiB
2026-02-15T20:01:48.018861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9929956
Coefficient of variation (CV)0.66436248
Kurtosis-1.2442825
Mean2.9998618
Median Absolute Deviation (MAD)2
Skewness0.0025656076
Sum65109
Variance3.9720315
MonotonicityNot monotonic
2026-02-15T20:01:48.237989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
23133
14.4%
13130
14.4%
33115
14.4%
53110
14.3%
43104
14.3%
63067
14.1%
03045
14.0%
ValueCountFrequency (%)
03045
14.0%
13130
14.4%
23133
14.4%
33115
14.4%
43104
14.3%
53110
14.3%
63067
14.1%
ValueCountFrequency (%)
63067
14.1%
53110
14.3%
43104
14.3%
33115
14.4%
23133
14.4%
13130
14.4%
03045
14.0%

Season
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.1 KiB
Monsoon
6573 
Summer
6035 
Winter
5568 
Post-Monsoon
3528 

Length

Max length12
Median length6
Mean length7.2781515
Min length6

Characters and Unicode

Total characters157965
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Monsoon6573
30.3%
Summer6035
27.8%
Winter5568
25.7%
Post-Monsoon3528
16.3%

Length

2026-02-15T20:01:48.624776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T20:01:48.780476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
monsoon6573
30.3%
summer6035
27.8%
winter5568
25.7%
post-monsoon3528
16.3%

Most occurring characters

ValueCountFrequency (%)
o33831
21.4%
n25770
16.3%
s13629
8.6%
m12070
 
7.6%
e11603
 
7.3%
r11603
 
7.3%
M10101
 
6.4%
t9096
 
5.8%
u6035
 
3.8%
S6035
 
3.8%
Other values (4)18192
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)157965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o33831
21.4%
n25770
16.3%
s13629
8.6%
m12070
 
7.6%
e11603
 
7.3%
r11603
 
7.3%
M10101
 
6.4%
t9096
 
5.8%
u6035
 
3.8%
S6035
 
3.8%
Other values (4)18192
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)157965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o33831
21.4%
n25770
16.3%
s13629
8.6%
m12070
 
7.6%
e11603
 
7.3%
r11603
 
7.3%
M10101
 
6.4%
t9096
 
5.8%
u6035
 
3.8%
S6035
 
3.8%
Other values (4)18192
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)157965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o33831
21.4%
n25770
16.3%
s13629
8.6%
m12070
 
7.6%
e11603
 
7.3%
r11603
 
7.3%
M10101
 
6.4%
t9096
 
5.8%
u6035
 
3.8%
S6035
 
3.8%
Other values (4)18192
11.5%

PM_ratio
Real number (ℝ)

High correlation  Skewed 

Distinct20653
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5944645
Minimum0.025408348
Maximum65.336066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:49.035460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.025408348
5-th percentile0.23422538
Q10.36632581
median0.4767896
Q30.60818057
95-th percentile1.2706554
Maximum65.336066
Range65.310657
Interquartile range (IQR)0.24185476

Descriptive statistics

Standard deviation1.2243971
Coefficient of variation (CV)2.059664
Kurtosis1409.1519
Mean0.5944645
Median Absolute Deviation (MAD)0.11905158
Skewness34.543685
Sum12902.257
Variance1.4991483
MonotonicityNot monotonic
2026-02-15T20:01:49.302759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.37598496816
 
< 0.1%
0.4141794675
 
< 0.1%
0.40393830475
 
< 0.1%
0.55545714094
 
< 0.1%
0.37422214514
 
< 0.1%
0.44549056174
 
< 0.1%
0.29053002344
 
< 0.1%
0.32511493234
 
< 0.1%
0.84053080734
 
< 0.1%
0.7471011874
 
< 0.1%
Other values (20643)21660
99.8%
ValueCountFrequency (%)
0.025408348461
< 0.1%
0.025518961891
< 0.1%
0.026806757461
< 0.1%
0.028476797771
< 0.1%
0.029044607941
< 0.1%
0.030135879341
< 0.1%
0.030145149211
< 0.1%
0.031059262831
< 0.1%
0.031646734131
< 0.1%
0.038461538461
< 0.1%
ValueCountFrequency (%)
65.336065571
< 0.1%
54.105691061
< 0.1%
53.116666671
< 0.1%
51.110236221
< 0.1%
47.573643411
< 0.1%
47.459677421
< 0.1%
47.10156251
< 0.1%
46.182539681
< 0.1%
46.150793651
< 0.1%
41.042016811
< 0.1%

PM2.5_rolling
Real number (ℝ)

High correlation 

Distinct21156
Distinct (%)97.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean66.491837
Minimum0.22666667
Maximum587.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.1 KiB
2026-02-15T20:01:49.539412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.22666667
5-th percentile14.5435
Q130.0775
median49.533333
Q380.075833
95-th percentile186.788
Maximum587.44
Range587.21333
Interquartile range (IQR)49.998333

Descriptive statistics

Standard deviation57.583686
Coefficient of variation (CV)0.86602639
Kurtosis8.3443399
Mean66.491837
Median Absolute Deviation (MAD)22.931667
Skewness2.4375645
Sum1443005.9
Variance3315.8808
MonotonicityNot monotonic
2026-02-15T20:01:50.030560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.4505779578
 
0.4%
93.714
 
< 0.1%
36.053333333
 
< 0.1%
77.43
 
< 0.1%
49.926666673
 
< 0.1%
16.073
 
< 0.1%
64.623333333
 
< 0.1%
44.293
 
< 0.1%
51.607051973
 
< 0.1%
39.553333333
 
< 0.1%
Other values (21146)21596
99.5%
ValueCountFrequency (%)
0.22666666671
< 0.1%
0.59666666671
< 0.1%
0.80333333331
< 0.1%
1.1033333331
< 0.1%
1.1233333331
< 0.1%
1.191
< 0.1%
2.221
< 0.1%
2.331
< 0.1%
2.7566666671
< 0.1%
3.0066666671
< 0.1%
ValueCountFrequency (%)
587.441
< 0.1%
551.00666671
< 0.1%
538.73333331
< 0.1%
516.81
< 0.1%
511.261
< 0.1%
507.26685931
< 0.1%
504.10333331
< 0.1%
501.941
< 0.1%
501.93333331
< 0.1%
471.651
< 0.1%

Interactions

2026-02-15T20:01:29.811781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:20.166214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:29.445617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:35.114673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:42.851439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:54.793959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:07.149915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:15.811213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:25.809250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:33.348277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:38.654236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:45.828336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:49.952525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:55.123656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:00.436002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:05.452718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:11.237818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:17.489840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:23.616614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:30.130752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:20.837797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:29.630085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:35.583044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:43.521117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:55.520660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:07.703990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:16.101344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:26.158794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:33.515045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:38.930561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:46.023457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:50.194160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:55.307717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:00.609651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:05.852382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:11.597346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:17.798388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:23.843039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:30.301370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:21.486345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:29.816818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:36.003510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:43.986745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:56.332970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:08.327391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:17.384850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:26.461685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:33.679258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:39.147165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:46.196588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:50.372104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:55.538761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:00.920424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:06.309980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:12.027437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:18.192910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:24.130619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:30.660528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:22.135655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:30.015665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:36.180449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:44.810726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:57.234281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:08.893115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:17.818155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:27.027917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:33.858055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:39.375058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:46.395908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:50.556933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:55.798519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:01.122679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:06.651963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:12.373023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:18.670049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:24.328808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:31.198323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:22.717342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:30.220608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:36.714129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:45.754653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:57.946376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:09.190039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:18.139361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:27.498915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:34.110815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:39.646238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:46.610934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:50.814888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:56.032862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:01.312442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:07.071465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:12.650047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:19.108891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:24.655033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:31.463511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:23.295268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:30.421589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:37.250729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:46.217736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:58.834243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:09.526278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:18.634794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:27.870600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:34.321730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:40.031897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:46.800065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:51.027026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:56.248157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:01.967639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:07.251639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:13.004834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:19.290872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:25.050187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:31.881876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:23.845312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:30.644496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:37.898811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:46.743543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:59.305278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:09.908167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:19.179629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:28.089313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:34.521298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:40.275952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:47.005209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:51.372738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:56.692148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:02.150711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:07.483209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:13.189353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:19.577004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:25.610257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:32.098296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:24.464133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:30.896599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:38.129477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:47.449214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:00.114156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:10.095942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:19.640943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:28.358776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-15T20:01:29.451081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:35.150412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:29.199300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:34.702049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:42.556822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T19:59:54.343827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:06.732354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:15.570904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:25.185727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:32.857628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:38.393895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:45.534741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:49.778765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:00:54.911502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:00.209491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:05.253307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:11.058052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:17.203803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:23.432170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T20:01:29.626302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-15T20:01:50.290924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AQIAQI_BucketBenzeneCOCityNH3NONO2NOxO3PM10PM2.5PM2.5_rollingPM_ratioSO2SeasonTolueneXylenedayday_of_weekmonthyear
AQI1.0000.6160.2040.5920.2470.3900.5070.5210.4930.2880.7540.8500.8540.3840.3860.1790.2570.050-0.019-0.000-0.032-0.281
AQI_Bucket0.6161.0000.0230.2670.3860.0980.2240.2730.2410.1490.4180.4370.4850.0140.2220.2400.1280.0540.0070.0000.1940.134
Benzene0.2040.0231.0000.1860.1210.0220.1840.2460.2030.1340.1750.1710.1590.0420.1210.0280.6220.233-0.002-0.0100.045-0.003
CO0.5920.2670.1861.0000.2130.2700.3900.2960.3740.0610.3810.4170.4090.1880.2880.0350.2700.152-0.010-0.0240.038-0.252
City0.2470.3860.1210.2131.0000.2190.1700.2200.2070.1800.2710.1830.1980.0500.2750.0770.1580.0800.0000.0000.0590.281
NH30.3900.0980.0220.2700.2191.0000.3200.3560.2560.1560.4090.3760.3890.0890.1310.0900.0060.0120.000-0.006-0.018-0.244
NO0.5070.2240.1840.3900.1700.3201.0000.5150.778-0.0520.4620.4580.4460.1170.3630.1500.2140.126-0.012-0.0150.058-0.068
NO20.5210.2730.2460.2960.2200.3560.5151.0000.6670.3250.4760.5070.4980.1740.2990.1830.2860.067-0.012-0.0180.004-0.140
NOx0.4930.2410.2030.3740.2070.2560.7780.6671.0000.0510.4460.4480.4370.1410.3600.1750.2800.102-0.012-0.0170.0140.036
O30.2880.1490.1340.0610.1800.156-0.0520.3250.0511.0000.2330.2810.2790.1270.2070.1440.1520.054-0.0080.000-0.108-0.025
PM100.7540.4180.1750.3810.2710.4090.4620.4760.4460.2331.0000.7610.7380.0200.2910.1970.1970.070-0.016-0.008-0.051-0.245
PM2.50.8500.4370.1710.4170.1830.3760.4580.5070.4480.2810.7611.0000.9490.5970.2920.2190.1790.076-0.011-0.009-0.037-0.254
PM2.5_rolling0.8540.4850.1590.4090.1980.3890.4460.4980.4370.2790.7380.9491.0000.5530.2850.2770.1690.066-0.0090.002-0.048-0.264
PM_ratio0.3840.0140.0420.1880.0500.0890.1170.1740.1410.1270.0200.5970.5531.0000.0910.0270.0210.0520.0030.0010.014-0.055
SO20.3860.2220.1210.2880.2750.1310.3630.2990.3600.2070.2910.2920.2850.0911.0000.0870.3010.0610.003-0.007-0.0360.114
Season0.1790.2400.0280.0350.0770.0900.1500.1830.1750.1440.1970.2190.2770.0270.0871.0000.0280.0440.0000.0000.9280.185
Toluene0.2570.1280.6220.2700.1580.0060.2140.2860.2800.1520.1970.1790.1690.0210.3010.0281.0000.198-0.004-0.0060.0580.074
Xylene0.0500.0540.2330.1520.0800.0120.1260.0670.1020.0540.0700.0760.0660.0520.0610.0440.1981.000-0.001-0.0030.035-0.013
day-0.0190.007-0.002-0.0100.0000.000-0.012-0.012-0.012-0.008-0.016-0.011-0.0090.0030.0030.000-0.004-0.0011.0000.0010.013-0.008
day_of_week-0.0000.000-0.010-0.0240.000-0.006-0.015-0.018-0.0170.000-0.008-0.0090.0020.001-0.0070.000-0.006-0.0030.0011.0000.004-0.001
month-0.0320.1940.0450.0380.059-0.0180.0580.0040.014-0.108-0.051-0.037-0.0480.014-0.0360.9280.0580.0350.0130.0041.000-0.279
year-0.2810.134-0.003-0.2520.281-0.244-0.068-0.1400.036-0.025-0.245-0.254-0.264-0.0550.1140.1850.074-0.013-0.008-0.001-0.2791.000

Missing values

2026-02-15T20:01:35.519913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-15T20:01:35.949851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CityDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucketyearmonthdayday_of_weekSeasonPM_ratioPM2.5_rolling
28Ahmedabad2015-01-2983.13118.1271036.9328.7133.7223.4834766.9349.5259.760.020.003.14209.0Poor20151293Winter0.697826NaN
29Ahmedabad2015-01-3079.84118.12710313.8528.6841.0823.48347613.8548.4997.070.040.004.81328.0Very Poor20151304Winter0.670209NaN
30Ahmedabad2015-01-3194.52118.12710324.3932.6652.6123.48347624.3967.39111.330.240.017.67514.0Severe20151315Winter0.79343885.830000
31Ahmedabad2015-02-01135.99118.12710343.4842.0884.5723.48347643.4875.23102.700.400.0425.87782.0Severe2015216Winter1.141554103.450000
32Ahmedabad2015-02-02178.33118.12710354.5635.3172.8023.48347654.5655.04107.380.460.0635.61914.0Severe2015220Winter1.496973136.280000
33Ahmedabad2015-02-03139.70118.12710330.6128.4056.7323.48347630.6133.7973.600.170.0311.87660.0Severe2015231Winter1.172697151.340000
34Ahmedabad2015-02-0480.65118.1271032.3722.8324.0023.4834762.3725.7347.300.000.000.00294.0Poor2015242Winter0.677008132.893333
35Ahmedabad2015-02-0558.36118.1271032.6021.3923.3123.4834762.6032.6653.540.000.000.00149.0Moderate2015253Winter0.48989792.903333
36Ahmedabad2015-02-0679.29118.1271031.1626.9426.8323.4834761.1667.4159.300.000.000.00190.0Moderate2015264Winter0.66559272.766667
37Ahmedabad2015-02-0788.70118.1271037.2931.3237.7323.4834767.2980.0944.760.000.000.00247.0Poor2015275Winter0.74458375.450000
CityDatePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucketyearmonthdayday_of_weekSeasonPM_ratioPM2.5_rolling
29521Visakhapatnam2020-06-2233.17108.225.5842.4527.0613.700.7313.6534.853.9900010.2400002.32000095.0Satisfactory20206220Monsoon0.30369928.600000
29522Visakhapatnam2020-06-2325.4083.382.7634.0919.9213.130.5410.4043.272.8800012.0300001.330000100.0Satisfactory20206231Monsoon0.30101928.176667
29523Visakhapatnam2020-06-2434.3690.901.2223.3813.1214.450.5610.9235.122.990003.1500001.60000086.0Satisfactory20206242Monsoon0.37388530.976667
29524Visakhapatnam2020-06-2513.4558.542.3021.6013.0912.270.418.1929.381.280005.6400000.92000077.0Satisfactory20206253Monsoon0.22589924.403333
29525Visakhapatnam2020-06-267.6332.275.9123.2717.1911.150.466.8719.901.450005.3700001.45000047.0Good20206264Monsoon0.22933618.480000
29526Visakhapatnam2020-06-2715.0250.947.6825.0619.5412.470.478.5523.302.2400012.0700000.73000041.0Good20206275Monsoon0.28918012.033333
29527Visakhapatnam2020-06-2824.3874.093.4226.0616.5311.990.5212.7230.140.740002.2100000.38000070.0Satisfactory20206286Monsoon0.32467715.676667
29528Visakhapatnam2020-06-2922.9165.733.4529.5318.3310.710.488.4230.960.010000.0100000.00000068.0Satisfactory20206290Monsoon0.34332420.770000
29529Visakhapatnam2020-06-3016.6449.974.0529.2618.8010.030.529.8428.300.000000.0000000.00000054.0Satisfactory20206301Monsoon0.32646721.310000
29530Visakhapatnam2020-07-0115.0066.000.4026.8514.055.200.592.1017.053.280848.7009723.07012850.0Good2020712Monsoon0.22388118.183333